From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data

Author:

Liu Ye1,Zhu Lei2,Pei Shunda1,Fu Huazhu3,Qin Jing4,Zhang Qing5,Wan Liang1,Feng Wei1

Affiliation:

1. Tianjin University, Tianjin, China

2. University of Cambridge, Cambridge, United Kingdom

3. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

4. Hong Kong Polytechnic University, Hong Kong , China

5. Sun Yat-sen University, Guangzhou, China

Funder

the National Natural Science Foundation of China

Innovation and Technology Fund - Midstream Research Programme for Universities (ITF-MRP)

the research fund for The Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China

Publisher

ACM

Cited by 72 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Learning depth-aware decomposition for single image dehazing;Computer Vision and Image Understanding;2024-11

2. Real-world Scene Image Enhancement with Contrastive Domain Adaptation Learning;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-09-06

3. Transmission-guided multi-feature fusion Dehaze network;The Visual Computer;2024-07-03

4. Frequency compensated diffusion model for real-scene dehazing;Neural Networks;2024-07

5. MixDehazeNet: Mix Structure Block For Image Dehazing Network;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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